Location is a primary cue in many context-aware computing systems, and is often represented as a global coordinate, room number, or Euclidean distance various landmarks. A user?s concept of location, however, is often defined in terms of regions in which common activities occur. We show how to partition a space into such regions based on patterns of observed user location and motion. These regions, which we call activity zones, represent regions of similar user activity, and can be used to trigger application actions, retrieve information based on previous context, and present information to users. We suggest that context-aware applications can benefit from a location representation learned from observing users. We describe an implementation of our system and present two example applications whose behavior is controlled by users? entry, exit, and presence in the zones.

This article describes a model for including scene/context priors in attention guidance. In the proposed scheme, visual context information can be available early in the visual processing chain, in order to modulate the saliency of image regions and to provide an efficient short cut for object detection and recognition. The scene is represented by means of a low-dimensional global description obtained from low-level features. The global scene features are then used to predict the probability of presence of the target object in the scene, and its location and scale, before exploring the image. Scene information can then be used to modulate the saliency of image regions early during the visual processing in order to provide an efficient short cut for object detection and recognition.

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.

This thesis introduces the Named-State Register File, a fine-grain, fully-associative register file. The NSF allows fast context switching between concurrent threads as well as efficient sequential program performance. The NSF holds more live data than conventional register files, and requires less spill and reload traffic to switch between contexts. This thesis demonstrates an implementation of the Named-State Register File and estimates the access time and chip area required for different organizations. Architectural simulations of large sequential and parallel applications show that the NSF can reduce execution time by 9% to 17% compared to alternative register files.

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple probabilistic framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.

The COntext INterchange (COIN) strategy is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. COIN has used its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web – both at the ontology level and at the data level. In conjunction with this, we propose a version of the COIN ontology model that uses OWL and the emerging rules interchange language, RuleML. / Singapore-MIT Alliance (SMA)

In this work the concept of 'context' is considered in five main points. First, context is seen as always necessary for an adequate explication of the concepts of meaning and understanding. Context always plays a role and is not merely brought into consideration when handling a special class of statements or terms, or when there is doubt and clarification is necessary. Second, context cannot be completely reduced to some system of representation. The reason for this is the presence of humans, which is always an important component of a context. Humans experience situations in ways that are not always reducible to symbolic representation. Third, contexts are in principle open. In normal cases they cannot be determined or described in advance. A context is not to be equated with a set of information. Fourth, we understand the parameters of a context pragmatically, which is why we are not led into doubt or even to meaning skepticism by the open nature of a context. This pragmatic knowledge belongs to the category of an ability. Fifth, contexts are, in principle, accessible. This denies the idea that some contexts are incommensurable. There are a number of pragmatic ways of accessing unfamiliar contexts. Some of these are here examined in light of the so-called 'culture wars' in the U.S.A. / Der Kontextbegriff wird so betrachtet, dass es in fünf Hauptpunkte untergliedert wird. Erstens: der Kontextbegriff ist für eine Explikation der Begriff die Bedeutung und des Verstehens immer notwendig. Der Kontext spielt immer eine Rolle und ist nicht nur für Fälle gut, in denen eine besondere Klasse von Wörtern behandelt wird, oder in denen Zweifel bestehen und eine Klarstellung benötigt wird. Zweitens: der Kontextbegriff lässt sich nicht vollständig auf eine Art von Repräsentation reduzieren. Grund dafür ist das Vorhandensein von Menschen, was immer ein wichtiger Bestandteil des Kontextes ist. Drittens: Kontexte sind grundsätzlich offen. Sie können nicht im Normalfall in vorbestimmter Art und Weise eingegrenzt oder beschrieben werden. Viertens: wir verstehen die Parameter eines Kontextes nach pragmatischer Art und Weise, daher führt uns die offene Natur eines Kontextes nicht zur epistemischen Zweifeln oder sogar Bedeutungsskeptizismus. Dieses pragmatische Wissen gehört in eine völlig andere Kategorie; die eine Fähigkeit ist. Fünftens: Kontexte sind prinzipiell zugänglich. Dies lehnt eine These der Inkommensurabilität zwischen Kontexte ab. Es gibt verschiedene pragmatishce Wege, um Zugang zu fremden Kontexte zu erreichen. Die sogenannte 'culure wars' in den U.S.A. werden hier als Beispiel bennant.

This is a qualitative study that includes participants that are in connection with a non-civic organization in Sweden. The study aims to capture the participants’ subjective experiences regarding human service organizations. The starting point is to catch both their positive and negative experiences, and then try to concretize what directed their perception in both ways. Is there any specific organization that is represented in positive and the negative column? Is it possible to explain the experience by looking at the organization and the organizational goals? Is it possible to see variables that might explain the perception? The study has a narrative focus that seeks the answers in the organisational context rather than in the stories themselves. The theoretical approach is Hasenfelds (1983) classification of human service organizations, and the “legal authority” that is one component in Weber’s (1983) bureaucracy. The result shows that it was one specific organization that dominated the negative experiences. Another aspect that seems to be important is that you meet the right person within these organizations.

Due to rapid development of the internet, it changes the life style of the human. The internet expert had reported that we are now in Web2.0 era. This research tries to explore how web GIS can fullfil the spirit of the Web2.0 and its possible applications. The first step of the research is to review the related techniques and applications of Geospatial web services and Web2.0. Then, a system with open GIS data structure was proposed and a web system was also established according to the spirit of Web2.0 - ¡§user participation¡¨. Web Map Services (WMS) and Web Feature Services (WFS) defined by Open Geospatial Consortium(OGC) were used in Geospatial web services system to search and view Geospatial data on the internet. Users can integrate spatial data from various sources on the internet and their own geospatial data and save as Web Map Context (WMC) file format. Then, WMC can be exchanged by other OGC Geospatial web services. In addition, the system supports file format transformation from WMC to KML, which is compatible with Google Earth. So users can use Google Earth to view the spatial layer information more easily. This study also developed a platform to demonstrate geospatial information in the blog, so users can share their Geospatial data in open GIS format with other bloggers. The system also use Google Map API and folksonomy in the data sharing process in order to speed up the web flow and to communicate their comments more easily.